Consumer-level software applications have expanded in scope and capability in recent years. A variety of spreadsheet, database, and other computational software packages are available to the business consumer and other markets. However, despite the availability of software applications of a variety of types and capabilities, large-scale spreadsheet and other computation-intensive applications are not always an effective or feasible tool for some applications. Consumer spreadsheet applications, for example, can sometimes be bogged down or possibly be made to crash by large-scale spreadsheet or matrix calculations, for instance, on the order of a 1000 by 1000 cell spreadsheets or larger. This can be due to, for example, the fact that even multiple-core desktop processors may not present enough computing throughput to process the large-scale computation required, for example, by linear regression, Monte Carl simulation, or other calculations. In other cases, the storage media on a desktop machine may also not be able to transfer data to or from processor or memory quickly enough to avoid bogging down during such large-scale computational tasks.
The advent of cloud-based computing resources has opened a new range of resources that computer users can subscribe to. Cloud-based networks permit users to arrange for the temporary usage of the collective networked assets of a set of resource servers from which processing, memory, software, or other resources may be extracted for a given period of time. In the case of machines attempting to execute a high-load application such as for instance a spreadsheet or data mining application, it may be desirable to provide methods and systems which permit an individual or small-scale client to promote the necessary computational load to a cloud-based network of highly scaled processing resources.